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A Structured Approach for Rapidly Mapping Multilevel System Measures via Simulation Metamodeling
Author(s) -
Rosen Scott L.,
Saunders Christopher P.,
Guharay Samar K
Publication year - 2015
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.21290
Subject(s) - metamodeling , computer science , construct (python library) , residual , statistic , metric (unit) , data mining , algorithm , mathematics , engineering , statistics , operations management , programming language
With increasing complexity of real‐world systems, especially for continuously evolving scenarios, systems analysts encounter a major challenge with the modeling techniques that capture detailed system characteristics defining input–output relationships. The models become very complex and require long time of execution. In this situation, techniques to construct approximations of the simulation model by metamodeling alleviate long run times and the need for large computational resources; it also provides a means to aggregate a simulation's multiple outputs of interest and derives a single decision‐making metric. The method described here leverages simulation metamodeling to map the three basic SE metrics, namely, measures of performance to measures of effectiveness to a single figure of merit. This enables using metamodels to map multilevel system measures supports rapid decision making. The results from a case study demonstrate the merit of the method. Several metamodeling techniques are compared and bootstrap error analysis and predicted residual sums of squares statistic are discussed to evaluate the standard error and error due to bias.